https://github.com/fork123aniket/molecule-graph-generation

Molecule Graph Generation using Graph Convolutional Networks-based Variational Graph AutoEncoders (VGAE) in PyTorch
https://github.com/fork123aniket/molecule-graph-generation

graph-convolutional-networks graph-neural-networks pytorch pytorch-geometric pytorch-tutorial variational-graph-auto-encoder

Last synced: about 1 year ago
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Molecule Graph Generation using Graph Convolutional Networks-based Variational Graph AutoEncoders (VGAE) in PyTorch

• Host: GitHub
• URL: https://github.com/fork123aniket/molecule-graph-generation
• Owner: fork123aniket
• License: mit
• Created: 2022-05-16T06:14:46.000Z (about 4 years ago)
• Default Branch: main
• Last Pushed: 2024-11-07T02:01:55.000Z (over 1 year ago)
• Last Synced: 2025-03-27T09:06:22.151Z (about 1 year ago)
• Topics: graph-convolutional-networks, graph-neural-networks, pytorch, pytorch-geometric, pytorch-tutorial, variational-graph-auto-encoder
• Language: Python
• Homepage:
• Size: 715 KB
• Stars: 23
• Watchers: 1
• Forks: 5
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Molecule Graph Generation

This repository aims at providing minimal implementation of Graph Convolutional Networks (GCNs) based Variational Graph AutoEncoders (VGAE) for molecule generation. VGAE is being used here to generate new molecular graphs that have similar statistical distribution as that of the learned distribution of the training dataset.

## Usage

### Requirements

- `Python 3.9`

- `PyTorch 1.8`

- `PyTorch Geometric 2.0.4`

- `numpy 1.22.3`

- `networkx 2.7.1`

### Data

***ZINC*** dataset from PyTorch Geometric (PyG) dataset collection is being used here. It contains 12k molecular graphs with 38 heavy atoms. Moreover, for each molecular graph, the node features are the types of heavy atoms and the edge features are the types of bonds between them. Besides, every single node in each of the molecular graphs represents an atom and the edge (link) between any two atoms represents the chemical bonding between them.

### Training, Validation, and Testing

The current implementation provides three imperative functions:-

- To train a new network : `train()`

- To validate the network being trained : `val()`

- To test a preTrained network : `test()`

- The average loss, area under curve score, and associated average precision for the trained model are printed after every epoch.

- All hyperparameters to control training and testing of the model, and generation of the molecular graphs are provided in the given `.py` file.

## Generated *ZINC* molecule graphs

`gen_graphs` variable is set to *5* in the current implementation to generate *5* new ***ZINC*** molecule graphs, however, it can be set to any number based on the requirements.

### Generated *ZINC* Molecule 1

![alt text](https://github.com/fork123aniket/Molecule-Graph-Generation/blob/main/Images/1.PNG)

### Generated *ZINC* Molecule 2

![alt text](https://github.com/fork123aniket/Molecule-Graph-Generation/blob/main/Images/2.PNG)

### Generated *ZINC* Molecule 3

![alt text](https://github.com/fork123aniket/Molecule-Graph-Generation/blob/main/Images/3.PNG)

### Generated *ZINC* Molecule 4

![alt text](https://github.com/fork123aniket/Molecule-Graph-Generation/blob/main/Images/4.PNG)

### Generated *ZINC* Molecule 5

![alt text](https://github.com/fork123aniket/Molecule-Graph-Generation/blob/main/Images/5.PNG)